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pacman::p_load(tmap, sf, DT, stplanr, tidyverse)Hands-on_EX10a
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odbus <- read_csv("/Users/yuhu/Desktop/Geospatial Analytics and Applications/Hands-on-Ex10/data/aspatial/origin_destination_bus_202210.csv")Rows: 5122925 Columns: 7
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (3): YEAR_MONTH, DAY_TYPE, PT_TYPE
dbl (4): TIME_PER_HOUR, ORIGIN_PT_CODE, DESTINATION_PT_CODE, TOTAL_TRIPS
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Code
glimpse(odbus)Rows: 5,122,925
Columns: 7
$ YEAR_MONTH <chr> "2022-10", "2022-10", "2022-10", "2022-10", "2022-…
$ DAY_TYPE <chr> "WEEKDAY", "WEEKENDS/HOLIDAY", "WEEKENDS/HOLIDAY",…
$ TIME_PER_HOUR <dbl> 10, 10, 7, 11, 16, 16, 20, 7, 7, 11, 11, 8, 11, 11…
$ PT_TYPE <chr> "BUS", "BUS", "BUS", "BUS", "BUS", "BUS", "BUS", "…
$ ORIGIN_PT_CODE <dbl> 65239, 65239, 23519, 52509, 54349, 54349, 43371, 8…
$ DESTINATION_PT_CODE <dbl> 65159, 65159, 23311, 42041, 53241, 53241, 14139, 9…
$ TOTAL_TRIPS <dbl> 2, 1, 2, 1, 1, 4, 1, 3, 1, 5, 2, 5, 15, 40, 1, 1, …Code
odbus$ORIGIN_PT_CODE <- as.factor(odbus$ORIGIN_PT_CODE)
odbus$DESTINATION_PT_CODE <- as.factor(odbus$DESTINATION_PT_CODE) Code
odbus6_9 <- odbus %>%
filter(DAY_TYPE == "WEEKDAY") %>%
filter(TIME_PER_HOUR >= 6 &
TIME_PER_HOUR <= 9) %>%
group_by(ORIGIN_PT_CODE,
DESTINATION_PT_CODE) %>%
summarise(TRIPS = sum(TOTAL_TRIPS))`summarise()` has grouped output by 'ORIGIN_PT_CODE'. You can override using
the `.groups` argument.Code
datatable(odbus6_9)Warning in instance$preRenderHook(instance): It seems your data is too big for
client-side DataTables. You may consider server-side processing:
https://rstudio.github.io/DT/server.htmlCode
busstop <- st_read(dsn = "/Users/yuhu/Desktop/Geospatial Analytics and Applications/Hands-on-Ex10/data/geospatial",
layer = "BusStop") %>%
st_transform(crs = 3414)Reading layer `BusStop' from data source
`/Users/yuhu/Desktop/Geospatial Analytics and Applications/Hands-on-Ex10/data/geospatial'
using driver `ESRI Shapefile'
Simple feature collection with 5159 features and 3 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 3970.122 ymin: 26482.1 xmax: 48284.56 ymax: 52983.82
Projected CRS: SVY21Code
mpsz <- st_read(dsn = "/Users/yuhu/Desktop/Geospatial Analytics and Applications/Hands-on-Ex10/data/geospatial",
layer = "MPSZ-2019") %>%
st_transform(crs = 3414)Reading layer `MPSZ-2019' from data source
`/Users/yuhu/Desktop/Geospatial Analytics and Applications/Hands-on-Ex10/data/geospatial'
using driver `ESRI Shapefile'
Simple feature collection with 332 features and 6 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 103.6057 ymin: 1.158699 xmax: 104.0885 ymax: 1.470775
Geodetic CRS: WGS 84Code
mpszSimple feature collection with 332 features and 6 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 2667.538 ymin: 15748.72 xmax: 56396.44 ymax: 50256.33
Projected CRS: SVY21 / Singapore TM
First 10 features:
SUBZONE_N SUBZONE_C PLN_AREA_N PLN_AREA_C REGION_N
1 MARINA EAST MESZ01 MARINA EAST ME CENTRAL REGION
2 INSTITUTION HILL RVSZ05 RIVER VALLEY RV CENTRAL REGION
3 ROBERTSON QUAY SRSZ01 SINGAPORE RIVER SR CENTRAL REGION
4 JURONG ISLAND AND BUKOM WISZ01 WESTERN ISLANDS WI WEST REGION
5 FORT CANNING MUSZ02 MUSEUM MU CENTRAL REGION
6 MARINA EAST (MP) MPSZ05 MARINE PARADE MP CENTRAL REGION
7 SUDONG WISZ03 WESTERN ISLANDS WI WEST REGION
8 SEMAKAU WISZ02 WESTERN ISLANDS WI WEST REGION
9 SOUTHERN GROUP SISZ02 SOUTHERN ISLANDS SI CENTRAL REGION
10 SENTOSA SISZ01 SOUTHERN ISLANDS SI CENTRAL REGION
REGION_C geometry
1 CR MULTIPOLYGON (((33222.98 29...
2 CR MULTIPOLYGON (((28481.45 30...
3 CR MULTIPOLYGON (((28087.34 30...
4 WR MULTIPOLYGON (((14557.7 304...
5 CR MULTIPOLYGON (((29542.53 31...
6 CR MULTIPOLYGON (((35279.55 30...
7 WR MULTIPOLYGON (((15772.59 21...
8 WR MULTIPOLYGON (((19843.41 21...
9 CR MULTIPOLYGON (((30870.53 22...
10 CR MULTIPOLYGON (((26879.04 26...Code
mpsz <- write_rds(mpsz, "/Users/yuhu/Desktop/Geospatial Analytics and Applications/Hands-on-Ex10/data/rds/mpsz.rds")Code
busstop_mpsz <- st_intersection(busstop, mpsz) %>%
select(BUS_STOP_N, SUBZONE_C) %>%
st_drop_geometry()Warning: attribute variables are assumed to be spatially constant throughout
all geometriesCode
datatable(busstop_mpsz)Code
write_rds(busstop_mpsz, "/Users/yuhu/Desktop/Geospatial Analytics and Applications/Hands-on-Ex10/data/rds/busstop_mpsz.rds") Code
od_data <- left_join(odbus6_9 , busstop_mpsz,
by = c("ORIGIN_PT_CODE" = "BUS_STOP_N")) %>%
rename(ORIGIN_BS = ORIGIN_PT_CODE,
ORIGIN_SZ = SUBZONE_C,
DESTIN_BS = DESTINATION_PT_CODE)Warning in left_join(odbus6_9, busstop_mpsz, by = c(ORIGIN_PT_CODE = "BUS_STOP_N")): Detected an unexpected many-to-many relationship between `x` and `y`.
ℹ Row 55491 of `x` matches multiple rows in `y`.
ℹ Row 161 of `y` matches multiple rows in `x`.
ℹ If a many-to-many relationship is expected, set `relationship =
"many-to-many"` to silence this warning.Code
duplicate <- od_data %>%
group_by_all() %>%
filter(n()>1) %>%
ungroup()Code
od_data <- unique(od_data)Code
od_data <- left_join(od_data , busstop_mpsz,
by = c("DESTIN_BS" = "BUS_STOP_N")) Warning in left_join(od_data, busstop_mpsz, by = c(DESTIN_BS = "BUS_STOP_N")): Detected an unexpected many-to-many relationship between `x` and `y`.
ℹ Row 74 of `x` matches multiple rows in `y`.
ℹ Row 1379 of `y` matches multiple rows in `x`.
ℹ If a many-to-many relationship is expected, set `relationship =
"many-to-many"` to silence this warning.Code
duplicate <- od_data %>%
group_by_all() %>%
filter(n()>1) %>%
ungroup()Code
od_data <- unique(od_data)Code
od_data <- od_data %>%
rename(DESTIN_SZ = SUBZONE_C) %>%
drop_na() %>%
group_by(ORIGIN_SZ, DESTIN_SZ) %>%
summarise(MORNING_PEAK = sum(TRIPS))`summarise()` has grouped output by 'ORIGIN_SZ'. You can override using the
`.groups` argument.Code
write_rds(od_data, "/Users/yuhu/Desktop/Geospatial Analytics and Applications/Hands-on-Ex10/data/od_data_fii.rds")Code
# Reading the file
od_data_fij <- read_rds("/Users/yuhu/Desktop/Geospatial Analytics and Applications/Hands-on-Ex10/data/od_data_fii.rds")Code
flowLine <- od2line(flow = od_data_fij,
zones = mpsz,
zone_code = "SUBZONE_C")Creating centroids representing desire line start and end points.Code
write_rds(flowLine, "/Users/yuhu/Desktop/Geospatial Analytics and Applications/Hands-on-Ex10/data/rds/flowLine.rds")Code
flowLine <- read_rds("/Users/yuhu/Desktop/Geospatial Analytics and Applications/Hands-on-Ex10/data/rds/flowLine.rds")Code
# Set the option to check and fix invalid polygons
tmap_options(check.and.fix = TRUE)
# Try plotting again
tm_shape(mpsz) +
tm_polygons() +
flowLine %>%
tm_shape() +
tm_lines(lwd = "MORNING_PEAK",
style = "quantile",
scale = c(0.1, 1, 3, 5, 7, 10),
n = 6,
alpha = 0.3)Warning: The shape mpsz is invalid. See sf::st_is_validWarning: The shape . is invalid. See sf::st_is_validWarning in g$scale * (x/maxW): longer object length is not a multiple of
shorter object length
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tm_shape(mpsz) +
tm_polygons() +
flowLine %>%
filter(MORNING_PEAK >= 5000) %>%
tm_shape() +
tm_lines(lwd = "MORNING_PEAK",
style = "quantile",
scale = c(0.1, 1, 3, 5, 7, 10),
n = 6,
alpha = 0.3)Warning: The shape mpsz is invalid. See sf::st_is_validWarning: The shape . is invalid. See sf::st_is_validWarning in g$scale * (x/maxW): longer object length is not a multiple of
shorter object length